Many people suffer from fractures of the bone, particularly elderly people. According to the findings of the International Osteoporosis Foundation, the lifetime risk for osteoporotic fractures in women is 30%-40% worldwide, and 13% in men. The number of hip fractures could rise from 1.7 million worldwide in 1990 to 6.3 million by 2050. Most dramatic increase is expected to occur in Asia during the next few decades. According to World Health Organization, osteoporosis is second only to cardiovascular disease as a leading health care problem.
In clinical practice, doctors and radiologists in large hospitals rely mainly on x-ray images to determine the occurrence and the precise nature of the fractures. Visual inspection of x-rays for fractures is a tedious and time-consuming process. Typically, the number of images containing fractures constitutes a small percentage of the total number of images that the radiologists have to inspect. For example, in a sample of x-ray images of femurs collected, only about 10% of them are fractured. After looking through many images containing healthy femurs, a tired radiologist has been found to miss a fractured case among the many healthy ones.
Some methods of bone fracture detection utilize non-visual techniques to detect fractures. This includes using acoustic pulses, mechanical vibration and electrical conductivity.
Furthermore, existing methods of bone fracture detection mostly fail to consider that the shapes and sizes of the bones are not identical. Even among healthy bones, there are still differences in the appearance because they are naturally-occurring objects. Age and gender also contribute to the difference in the appearance of the bones. One standard method of dealing with size variation is to normalize the size of the bones in the captured x-ray images for visual inspection. This method is, however, unsatisfactory because it can either remove important texture information (if the image is shrunken) or introduce noise and artifacts (if the image is enlarged).